code: def setUpClass(cls):
      cls.setup = yaml.load(
          open(os.path.join("tests", "data", "config.yml"), "r")
      )
      cls.weights_path = {
          "generator": os.path.join(
              cls.setup["weights_dir"], "test_gen_weights.hdf5"
          ),
          "discriminator": os.path.join(
              cls.setup["weights_dir"], "test_dis_weights.hdf5"
          ),
      }
      cls.hr_shape = (cls.setup["patch_size"] * 2,) * 2 + (3,)
  
      cls.RRDN = RRDN(
          arch_params=cls.setup["rrdn"],
          patch_size=cls.setup["patch_size"],
      )
      cls.RRDN.model.compile(optimizer=Adam(), loss=["mse"])
      cls.RDN = RDN(
          arch_params=cls.setup["rdn"],
          patch_size=cls.setup["patch_size"],
      )
      cls.RDN.model.compile(optimizer=Adam(), loss=["mse"])
      cls.f_ext = Cut_VGG19(
          patch_size=cls.setup["patch_size"] * 2,
          layers_to_extract=[1, 2],
      )
      cls.f_ext.model.compile(optimizer=Adam(), loss=["mse", "mse"])
      cls.discr = Discriminator(patch_size=cls.setup["patch_size"] * 2)
      cls.discr.model.compile(optimizer=Adam(), loss=["mse"])
  
file_path: image-super-resolution/tests/models/test_models.py
imports: import os
  import unittest
  import yaml
  import numpy
  from tensorflow.keras.optimizers import Adam
  from ISR.models.rrdn import RRDN
  from ISR.models.rdn import RDN
  from ISR.models.discriminator import Discriminator
  from ISR.models.cut_vgg19 import Cut_VGG19
label_desc: Use image node_id 1 for calling the ModelsClassTest obj's underlying member method code with example usage: obj.setUpClass(cls) without return types